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1 -XWiki.robing
1 +XWiki.denker
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9 9  
10 10  Robin Gutzen^^1^^, Giulia De Bonis^^2^^, Elena Pastorelli^^2,3^^, Cristiano Capone^^2^^,
11 11  
12 -Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1,4^^,
12 +Chiara De Luca^^2,3^^, Michael Denker^^1^^, Sonja Grün^^1^^,
13 13  
14 -Pier Stanislao Paolucci^^2^^, Andrew Davison^^5^^
14 +Pier Stanislao Paolucci^^2^^, Andrew Davison^^4^^
15 15  
16 -Experiments: Anna Letizia Allegra Mascaro^^6,7^^, Francesco Resta^^6^^, Francesco Saverio Pavone^^6^^, Maria-Victoria Sanchez-Vives^^8,9^^
16 +Experiments: Anna Letizia Allegra Mascaro^^5,6^^, Francesco Resta^^5^^, Francesco Saverio Pavone^^5^^, Maria-Victoria Sanchez-Vives^^7,8^^
17 17  
18 18  ,,1) Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Centre, Jülich, Germany,,
19 19  
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21 21  
22 22  ,,3) Ph.D. Program in Behavioural Neuroscience, “Sapienza” University of Rome, Rome, Italy,,
23 23  
24 -,,4) Theoretical Systems Neurobiology, RWTH Aachen University, Aachen, Germany,,
24 +,,4) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
25 25  
26 -,,5) Unité de Neurosciences, Information et Complexité, Neuroinformatics Group, CNRS FRE 3693, Gif-sur-Yvette, France,,
26 +,,5) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
27 27  
28 -,,6) European Laboratory for Non-linear Spectroscopy (LENS), (% style="--darkreader-inline-color:inherit; color:inherit" %)University of Florence, Florence, Italy(%%),,
28 +,,6) Istituto di Neuroscienze, CNR, Pisa, Italy,,
29 29  
30 -,,7) Istituto di Neuroscienze, CNR, Pisa, Italy,,
30 +,,7) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
31 31  
32 -,,8) Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain,,
33 -
34 -,,9) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
32 +,,8) Institució Catalana de Recerca i Estudis Avanc ̨ats (ICREA), Barcelona, Spain,,
35 35  )))
36 36  )))
37 37  
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41 41  (((
42 42  == Flexible workflows to generate multi-scale analysis scenarios ==
43 43  
44 -This collab illustrates the usage of the [[Neo>>https://neo.readthedocs.io/en/stable/]] and [[Elephant>>https://elephant.readthedocs.io/en/latest/]] tools in performing data analysis with regards to the SGA2-SP3-UC002 KR3.2, investigating sleep, anesthesia, and the transition to wakefulness: see Chapter 1 and Figure 2  of SGA2[[ Deliverable D3.2.1.>>https://drive.ebrains.eu/smart-link/17ac0d6e-e050-4a49-8ca2-e223b70a3121/]], for an overview of the scientific motivations and a description of the UseCase workflow; Chapter 2 (same document) for an introduction to KR3.2; Chapter 3, for a description of the mice ECoG data sets; Chapter 5, about the Slow Wave Analysis Pipeline and Chapter 6 for the mice wide-field GECI data). For details on the datasets used in this collab, please see the References below.
42 +This Collab is aimed at experimental and computational neuroscientists interested in the usage of the [[Neo>>https://neo.readthedocs.io/en/stable/]] and [[Elephant>>https://elephant.readthedocs.io/en/latest/]] tools in performing data analysis of spiking data.
43 +Here, the collab illustrates the tool usage with regards to the SGA2-SP3-UC002 KR3.2, investigating sleep, anesthesia, and the transition to wakefulness: see Chapter 1 and Figure 2  of SGA2[[ Deliverable D3.2.1.>>https://drive.ebrains.eu/smart-link/17ac0d6e-e050-4a49-8ca2-e223b70a3121/]], for an overview of the scientific motivations and a description of the UseCase workflow; Chapter 2 (same document) for an introduction to KR3.2; Chapter 3, for a description of the mice ECoG data sets; Chapter 5, about the Slow Wave Analysis Pipeline and Chapter 6 for the mice wide-field GECI data)
45 45  
45 +[[image:https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png||height="35" width="35"]][[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]]
46 +
46 46  == How the pipeline works ==
47 47  
48 48  The design of the pipeline aims at interfacing a variety of general and specific analysis and processing steps in a flexible modular manner. Hence, it enables the pipeline to adapt to diverse types of data (e.g., electrical ECoG, or optical Calcium Imaging recordings) and to different analysis questions. This makes the analyses a) more reproducible and b) comparable amongst each other since they rely on the same stack of algorithms and any differences in the analysis are fully transparent.
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74 74  
75 75  * **Run the notebook**
76 76  In the jupyter hub, navigate to //drive/My Libraries/My Library/pipeline/showcase_notebooks/run_snakemake_in_collab.ipynb//, or where you copied the //pipeline// folder to.
77 -Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake.
78 +Follow the notebook to install the required packages into your Python kernel, set the output path, and execute the pipeline with snakemake
78 78  
79 79  === ii) Local execution ===
80 80  
81 -//tested only with Mac OS and Linux!//
82 -
83 83  * **Get the code**
84 84  The source code of the pipeline is available via Github: [[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]] and can be cloned to your machine ([[how to get started with Github>>https://guides.github.com/activities/hello-world/]]).
85 85  
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88 88  In the wavescalephant git repository, there is an environment file ([[pipeline/environment.yaml>>https://drive.ebrains.eu/smart-link/1a0b15bb-be87-46ee-b838-4734bc320d20/]]) specifying the required packages and versions. To build the environment, we recommend using conda ([[how to get started with conda>>https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html]]).
89 89  ##conda env create ~-~-file environment.yaml
90 90  conda activate wavescalephant_env##
91 -
92 -Make sure that neo and elephant were installed as their Github development version, and if necessary add them manually to the environment.
93 -##pip install git+https:~/~/github.com/NeuralEnsemble/elephant.git
94 -pip install git+https:~/~/github.com/NeuralEnsemble/python-neo.git##
95 95  
96 96  )))
97 97  * **Edit the settings**
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119 119  
120 120  All results are stored in the path specified in the //settings.py// file. The folder structure reflects the structuring of the pipeline into stages and blocks. All intermediate results are stored as //.nix// files using the [[Neo data format>>https://neo.readthedocs.io/en/stable/]] and can be loaded with ##neo.NixIO('/path/to/file.nix').read_block()##. Additionally, most blocks produce a figure, and each stage a report file, to give an overview of the execution log, parameters, intermediate results, and to help with debugging. The final stage (//stage05_wave_characterization//) stores the results as[[ //pandas.DataFrames//>>https://pandas.pydata.org/]] in //.csv// files, separately for each measure as well as in a combined dataframe for all measures.
121 121  
122 -**Examples of the output figures (for IDIBAPS dataset)**
117 +== Outlook ==
123 123  
124 -* Stage 01 - [[example signal traces and metadata>>https://drive.ebrains.eu/smart-link/cf2fa914-260d-4d61-a2da-03ea07b7f9be/]]
125 -* Stage 02 - [[background substraction>>https://drive.ebrains.eu/smart-link/586d2f3c-591b-4dfb-94ee-8c0e28050dc4/]]
126 -* Stage 02 - [[logMUA estimation>>https://drive.ebrains.eu/smart-link/c92e4b0c-0938-44e8-9f8d-00522796b2fd/]]
127 -* Stage 02 - [[processed signal trace>>https://drive.ebrains.eu/smart-link/26ed27c6-de56-4b48-a57b-f70aab629197/]]
128 -* Stage 03 - [[amplitude distribution>>https://drive.ebrains.eu/smart-link/8ba80293-ba75-4a37-8a8f-05d44cf6f65c/]]
129 -* Stage 03 - [[UP state detection>>https://drive.ebrains.eu/smart-link/ab172be0-178e-4153-a3e6-b4bace32dd50/]]
130 -* Stage 04 - [[trigger clustering>>https://drive.ebrains.eu/smart-link/4a1f0169-8b43-49ce-80c8-f2fa0f4d50d3/]]
131 -* Stage 05 - [[planar velocities>>https://drive.ebrains.eu/smart-link/f4de8073-cb40-47a7-bc82-f97d36dbae25/]]
132 -* Stage 05 - [[directionality>>https://drive.ebrains.eu/smart-link/5485032d-0121-4cde-9ea2-3e0af3f12178/]]
133 -
134 -=== Outlook ===
135 -
136 136  * Using the **KnowledgeGraph API **to insert data directly from the Knowledge Graph into the pipeline and also register and store the corresponding results as Analysis Objects. Such Analysis Objects are to incorporate **Provenance Tracking, **using [[fairgraph>>https://github.com/HumanBrainProject/fairgraph]],** **to record the details of the processing and analysis steps.
137 137  * Adding support for the pipeline to make use of **HPC** resources when running on the collab.
138 138  * Further extending the available **methods** to address a wider variety of analysis objectives and support the processing of other datatypes. Additional documentation and guides should also make it easier for non-developers to contribute new method blocks.
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147 147  * [[Sanchez-Vives, M. (2020). "Propagation modes of slow waves in mouse cortex".  //EBRAINS//>>https://doi.org/10.25493/WKA8-Q4T]]
148 148  * [[Sanchez-Vives, M. (2019). "Cortical activity features in transgenic mouse models of cognitive deficits (Fragile X Syndrome).//" EBRAINS//>>https://doi.org/10.25493/ANF9-EG3]]
149 149  
150 -Code developed at:
151 -
152 -[[image:https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png||height="35" width="35"]][[INM-6/wavescalephant>>https://github.com/INM-6/wavescalephant]]
153 -
154 154  == License ==
155 155  
156 156  Text is licensed under the Creative Commons CC-BY 4.0 license. LENS data is licensed under the Creative Commons CC-BY-NC-ND 4.0 license. IDIBAPS data is licensed under the Creative Commons CC-BY-NC-SA 4.0 license. Software code is licensed under GNU General Public License v3.0.
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157 157  
158 158  [[image:https://i.creativecommons.org/l/by/4.0/88x31.png||style="float:left"]]
159 159  
160 -[[image:https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
139 + [[image:https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
161 161  
162 162  [[image:https://licensebuttons.net/l/by-nc-nd/4.0/88x31.png||alt="https://i.creativecommons.org/l/by/4.0/88x31.png" style="float:left"]]
163 163  
Collaboratory.Apps.Collab.Code.CollabClass[0]
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1 -Use Case SGA2-SP3-002 KR3.2: Integrating multi-scale data and the output of simulations in a reproducible and adaptable pipeline
1 +Space for developing and hosting a showcase pipeline for performing reproducible and adaptable analysis with the focus of slow cortical waves.
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